Visual Analysis of Floating Taxi Data Based on Interconnected and Timestamped Area Selections

Abstract

Floating Car Data (FCD) is GNSS-tracked vehicle movement, includes often large data size and is difficult to handle, especially in terms of visualization. Recently, FCD is often the base for interactive traffic maps for navigation and traffic forecasting. Handling FCD includes problems of large computational efforts, especially in case of connecting tracked vehicle positions to digitized road networks and subsequent traffic state derivations. Established interactive traffic maps show one possible visual representation for FCD. We propose a user-adapted map for the visual analysis of massive vehicle movement data. In our visual analysis approach we distinguish between a global and a local view on the data. Global views show the distribution of user-defined selection areas, in the way of focus maps. Local views show user-defined polygons with 2-D and 3-D traffic parameter visualizations and additional diagrams. Each area selection is timestamped with the time of its creation by the user. After defining a number of area selections it is possible to calculate weekday-dependent travel times based on historical taxi FCD. There are 3 different types of defined connections in global views. This has the aim to provide personalization for specific commuters by delivering only traffic and travel time information for and between user-selected areas. In a case study we inspect traffic parameters based on taxi FCD from Shanghai observed within 15 days in 2007. We introduce test selection areas, calculate their average traffic parameters and compare them with recent (2015) and typical traffic states coming from the Google traffic layer.

Understanding the relationship between complicated crossings and frequently visited locations – a case study with boro taxis in Manhattan

“…Abstracted road networks may imply less spatial (position and size) and semantical (driving directions, restrictions, lane number and connectivity) information than usual OSM road networks, as for the case of the GraphStream network of Le Havre (Sladewski, Keler, and Divanis 2017). Compared to these examples, the OSM road network is relatively accurate and rich on additional information on the number of lanes, road type and speed limits (Keler and Krisp 2016a).…”

Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC

“…One possibility for gaining more insights on urban human mobility is analyzing data from tracked entities, namely daily urban traffic participants. Vehicle movement trajectories of urban vehicle fleets can help predicting periodical travel time variations (Keler and Krisp 2016a) or classifying traffic congestion events by intensity (Keler, Ding, and Krisp 2016). Due to the massive size of data generated by vehicle or bicycle fleets, often only extracts are used in many analyses.…”